airl inverse reinforcement

This will help MPPI or any gradient-based optimal controller to find a better solution which drives the vehicle to stay in the middle of the road (the lowest cost area). For example, if the task is learning to visually track an object, the network will implicitly find the object as an important feature. The averaged activation map (heat map) of each pixel in the middle layer of E2EIL network is used to generate a costmap. Δt=0.02,T=60,Σsteer=0.3,Σthrottle=0.35,Cspeed=1.8, and Ccrash=0.9. These methods were compared over various real and simulated datasets including the TORCS open source driving simulator [31] dataset and the KITTI dataset [8]. Inverse Reinforcement Learning (AIRL) assumes that the reward is solely a function of state variables, and is inde-pendent of actions. While vision-based controls are harder to analytically write equations for when compared to positional-based controls, it has been shown to work by millions of humans using it every day. The binary filter outputs 1 if the activation is greater than 0. The proposed process allows for simple training and robustness to out-of-sample data. Specifically in vision-based autonomous driving, if we train a deep NN by imitation learning and analyze an intermediate layer by reading the weights of the trained network and the activated neurons of it, we see the mapping converged to extracting important features that link the input and the output (Fig. 1 with a running cost Eq. vx and vdx are measured body velocity in the x direction and desired velocity respectively. By separating the perception and low-level control into two robust components, this system can be more resilient to small errors in either. 1.1 Adversarial IRL (AIRL) Inverse reinforcement learning (IRL [7, 8] seeks to identify the re-ward function under which the expert policies are “optimal”. This feature extraction is further discussed in Section IV-C. Drews [7] provides a template NN architecture and training procedure to try to generalize costmap prediction to new environments in a method we call Attention-based Costmap Prediction (ACP). The optimal control is solved in a receding horizon fashion within an MPC framework to give us a real-time optimal control sequence u0,1,2,...T−1. Section III introduces the Model Predictive Path Integral (MPPI) control algorithm in image space and in Section IV-C, we introduce our Approximate Inverse Reinforcement Learning algorithm. We show that this method has the ability to learn \emph {generalizable} policies and reward functions in complex transfer learning tasks, while yielding results in continuous control benchmarks that are … This can be viewed as an implicit image segmentation done inside the deep convolutional neural network where the extracted features will depend on the task at hand. Literally, E2EIL trains agents to directly output optimal control actions given image data from cameras; End(sensor reading) to End(control). From Track E, we report a failure case of our method where the vehicle could not proceed to move forward. This step still requires some hand-tuning; for example, picking proper basis functions to form the distribution. Loquercio et al. Going off the image plane does not have a cost associated with it. This is most likely due to the images creating a feature space not seen in training. In this work, we leverage one such method, Adversarial Inverse Reinforcement Learning (AIRL), to propose an algorithm that learns hierarchical disentangled rewards with a policy over options. We perform a sampling-based stochastic optimal control in image space, which is perfectly suitable for our driver-view binary costmap. Aligning this … Chainer implementation of Adversarial Inverse Reinforcement Learning (AIRL) and Generative Adversarial Imitation Learning (GAIL). manipulator, drone), are possible applications of the proposed approach. MPC-based optimal controllers In this work, we propose adverserial inverse reinforcement learning (AIRL), a practical and scalable inverse reinforcement learning algorithm based on an adversarial reward learning formulation. However, inverse reinforcement learning methods have Russell [24] and Arora and Doshi [2] also describes how a learned reward function is more transferable than an expert policy because as a policy can be easily affected by different transition functions T whereas the reward function is can be considered a description of the ideal policy. Maximum entropy inverse reinforcement learning (MaxEnt IRL) (Ziebart et al., 2008) provides a general probabilistic framework to solve the ambiguity by finding the trajectory distribution with maximum entropy that matches the reward expectation of the experts. However, even with the online scheme of collecting datasets, it is impossible to experience all kinds of unexpected scenarios. From these reasons, E2E IL controllers are not widely used in the real-world applications, such as self-driving cars. We compare the methods mentioned in Section IV on the following scenarios: For a fair comparison, we trained all models with the same dataset used in [6]. In previous work, however, AIRL has mostly been demonstrated on robotic control in artificial environments. ACP produced clear cost maps models in Track A (which it was trained on) and Track C, though Track C’s costmap was incorrect. The vehicle is located at the bottom middle of the costmap and black represents the low-cost region, white represents the high-cost. The proposed methodology relies on Imitation Learning, Model Predictive Control (MPC), and Deep Learning. In this paper, we propose MA-AIRL, a new framework for multi-agent inverse reinforcement learning, It is important to note that near-perfect state estimation and a GPS track map is provided when MPPI is used as the expert, but as in [7], only body velocity, roll, and yaw from the state estimate is used when it is operating using vision. In this work, we used a model predictive optimal controller, MPPI [30], as the expert for E2EIL. In this work, we will use sections of a network trained with End-to-End Imitation Learning (E2EIL) using MPC as the expert policy. a) b) 2. 4: where Cs,Cc are coefficients that represent the penalty applied for speed and crash, respectively. As MaxEnt IRL requires solving an integral over all possible trajectories for computing the partition function, it is only suitable for small scale … However, their method is still best applied to drones where it is relatively easy to match a desired direction and velocity. However, it is important to note that, unlike in IL, the learning agents could then potentially outperform the expert behavior. Drews [7] uses an architecture that separates the vision-based control problem into a costmap generation task and then uses an MPC controller for generating the control. In IL, a policy is trained to accomplish a specific task by mimicking an expert’s control policy, which in most cases, is assumed to be optimal. While this is a valid assumption in most cases of driving, there are times where the coupling of steering and throttle is necessary to continue driving on the road. The resulting costmap is used in conjunction with a Model Predictive Controller for real-time control and outperforms other state-of-the-art costmap generators combined with MPC in novel environments. Proceedings of the 28th International Conference on Machine Learning, Stanley: The robot that won the DARPA Grand Challenge, Introductory techniques for 3-d computer vision, G. Williams, P. Drews, B. Goldfain, J. M. Rehg, and E. A. Theodorou, Aggressive driving with model predictive path integral control, 2016 IEEE International Conference on Robotics and Automation (ICRA), G. Williams, N. Wagener, B. Goldfain, P. Drews, J. M. Rehg, B. RL is one way to train agents to maximize some notion of task-specific rewards. 2). The maximum entropy reinforcement learning (MaxEnt RL) objective is defined as: max ˇ XT t=1 E (s t;a t)˘ˆ ˇ [r(s t;a)+ H(ˇ(js t))] (1) which augments the reward function with a causal entropy regularization term H(ˇ) = E ˇ[ logˇ(ajs)]. Both approaches will result in a similar behavior of collision-averse navigation, but since our paper focuses on generating a costmap, Both approaches will result in a similar behavior of collision-averse navigation, but since our paper focuses on generating a costmap. The major contributions over [5, 6] are using a Conv-LSTM layer to maintain the spatial information of states close together in time as well as a softmax attention mechanism applied to sparsify the Conv-LSTM layer. Approximate Inverse Reinforcement Learning, in this optimal control settings corresponds to the negative reward (. [6] learns to generate a costmap from camera images with a bottleneck network structure using a Long Short Term Memory (LSTM) layer. After this verification of MPPI parameters, we applied the same parameters to ACP. (a)a), and output. Since optimal controllers can be considered as a form of model-based RL, this ^R can then be used as the cost function that our MPC controller optimizes with respect to. Used in the Variational Discriminator Bottleneck (VDB) paper at ICLR.. Getting Set Up. Its extension to multi-agent settings, however, is difficult due to the more complex notions of rational behaviors. In this paper, we provide evidence of better performance than the expert teacher by showing a higher success rate of task completion when a task requires generalization to new environments. We show that this method has the ability to learn \emph{generalizable} policies and reward functions in complex transfer learning tasks, while yielding results in continuous control benchmarks that are comparable to those of the state-of-the-art methods. We interpret this intermediate stage, the activated heatmap, as important features, which relates the input and the output. provide planned control trajectories given an initial state and a cost function by solving the optimal control problem. Our idea of extracting middle layers of CNNs and using them as a costmap generator can be used to boost the training procedure of end-to-end controllers; if we use a known costmap to train an end-to-end controller, using moment matching like in [26, 12], we can train a deep CNN controller with two loss functions, one to fit a costmap in the middle layer and the other with the final action at the output. In this E2E control approach, we only need to query the expert’s action to learn a costmap of a specific task. In all of the sim datasets (Tracks D and E), it did not move. They separate their system into a perception and control pipeline. Section V details vision-based autonomous driving experiments with analysis and comparisons of the proposed methods. It works well in navigation along with a model predictive controller, but the MPC only solves an optimization problem with a local costmap. In classic path planning of robotic systems, sensor readings and optimization are all done in a world coordinate frame. The concise description of this work is to create a NN that can take in camera images and output a costmap used by a MPC controller. This technique is most related to our approach since they applied a learned color-to-cost mapping to transform a raw image into a costmap-like image, and performed path planning directly in the image space. . Maximum Entropy Inverse Reinforcement Learning (MaxEnt IRL). Inverse reinforce-ment learning provides a framework to automati-cally acquire suitable reward functions from ex-pert demonstrations. In the costmap learning approach ACP, Drews [7] uses deep learning to replace the pipeline a), and uses an MPC controller to handle b). After training, each neuron’s activation from the middle layer tells us the relevance of input, important features (Fig. Under the optimal control settings, we view these relevant features as cost function-related features, the intermediate step between the observation and the final optimal decision. The proposed method allows to avoid manually designing a cost map that is generally required in supervised learning. 5. However, a road-detection network requires manual labeling of what is a road and what is not. The data set consists of a vehicle running around a 170m-long track shown in Fig. Therefore, the problem simplifies from computing a good action to computing a good approximation of the cost function. 3. E2E learning has been shown to work in various lane-keeping applications [17, 3, 33]. A notable contribution is the ability to work in areas where positional information such as GPS or VICON data is not possible to obtain. Finally, we conclude and discuss future directions in Section VI and Section VII. We choose to use a sampling-based stochastic optimal controller MPPI [30] because it can operate on non-linear learned dynamics and can have non-convex cost functions. We extract middle convolutional layers from the trained E2EIL network and use them as a costmap for MPC. Then, after converting the X,Y,Z-axes to follow the convention in the computer vision community through Tr→c, the projection matrix Tc→f→p converts the camera coordinates to the pixel coordinates. Adversarial Inverse Reinforcement Learning (AIRL) [1] can be used to train agents to achieve high performance in sequential decision-making tasks with demonstration examples. The input image size is 160×128×3 and the output costmap from the middle layer is 40×32. Inverse Reinforcement Learning Michael Bloem and Nicholas Bambos Abstract—We extend the maximum causal entropy frame-work for inverse reinforcement learning to the infinite time horizon discounted reward setting. All hardware experiments were conducted using the 1/5 scale AutoRally autonomous vehicle test platform [10]. GAIL, AIRL) are mostly verified with control tasks in OpenAI Gym. Our approach outperforms other state-of-the-art vision and deep-learning-based controllers in generalizing to new environments. Boots, and E. A. Theodorou, Information theoretic MPC for model-based reinforcement learning, 2017 IEEE International Conference on Robotics and Automation (ICRA), B. Wymann, C. Dimitrakakisy, A. Sumnery, and C. Guionneauz, S. Xingjian, Z. Chen, H. Wang, D. Yeung, W. Wong, and W. Woo, Convolutional lstm network: a machine learning approach for precipitation nowcasting, Advances in neural information processing systems, End-to-end learning of driving models from large-scale video datasets, Proceedings of the IEEE conference on computer vision and pattern recognition, TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, Aggressive Deep Driving: Combining Convolutional Neural Networks and Model Predictive Control, End-to-End Training of Deep Visuomotor Policies. While IL provides benefits in terms of sample efficiency, it does have drawbacks. driving too close to the road boundaries). We also show Our work is obtaining a costmap based on an intermediate convolutional layer activation, but the middle layer output is not directly trained to predict a costmap; instead, it is generating an implicit objective function related to relevant features. Finally, we get the T matrix, which transforms the world coordinates to the pixel coordinates: To obtain the vehicle (camera) position in the pixel coordinates (u,v): However, this coordinate-transformed point [u′,v′] in the pixel coordinates has the origin at the top left corner of the image. The coordinate transformation consists of 4 steps: In this work, we follow the convention in the computer graphics community and set the Z (optic)-axis as the vehicle’s longitudinal (roll) axis, the Y-axis as the axis normal to the road, the positive direction being upwards, and the X-axis as the axis perpendicular on the vehicle’s longitudinal axis, the positive direction pointing to the right side of vehicle. Since the training data was collected at Track A (Fig. Then a cost weighted average is computed over the sampled controls. We first ran our costmap models AIRL and ACP on various datasets to show reasonable outputs in varied environments. We then took all three methods and drove them on Tracks B, C, D, and E.For Tracks B, D, and E,we ran each algorithm in both clockwise and counter-clockwise for 20 lap attempts and measured the average travel distance. More specifically, we focus on lane-keeping and collision checking like in [5, 6, 7, 22, 3]. The perception pipeline was a Convolutional Neural Network (CNN), taking in raw images and producing a desired direction and velocity, trained in simulation on a large mixture of random backgrounds and gates. In this work, we introduce a method for an inverse reinforcement learning problem and the task is vision-based autonomous driving. Generative Adversarial Imitation Learning (GAIL) is an efficient way to learn sequential control strategies from demonstration. Our main contribution is learning an approximate, ‘generalizable’ costmap ‘from’ E2EIL with a minimal extra cost of adding a binary filter. Their final model is trained on a mixture of real datasets of a simple racetrack as well as simulation datasets from a more complex track. In this sense, the reward function in an MDP encodes the task of the agent. T was set to correlate to approximately 6m long trajectories, as this covers almost all the drivable area in the camera view (see Fig. During training, the model implicitly learns a mapping from sensor input to the control output. In general, a discrete-time optimal control problem whose objective is to minimize a task-specific cost function J(x,u) can be formulated as follows: subject to discrete time, continuous state-action dynamical system. Despite these difficulties, IRL can be an extremely useful tool. Then, we construct the rotation matrices around the U, V, W-axis RU,RV,RW, the translation matrix Ttl, the robot-to-camera coordinate transformation matrix Tr→c and the projection matrix Tc→f→p as: where the projection matrix Tc→f→p projects the point (X,Y,Z) in the camera coordinates into the film coordinates using the perspective projection equations from [28] and the offsets oX and oY transform the film coordinates to the pixel coordinates by shifting the origin. We also tested blurring the features in the input image space, so that the pixels close to the important features are also relevant. Measurement of an agent’s behavior over time, in a variety of circumstances. Al-though reinforcement learning methods promise to learn such behaviors automatically, they have been most successful in tasks with a clear definition of the reward function. This result is shifted down a time step and used as the nominal trajectory to sample controls from in the next optimization round. J Chakravorty, N Ward, et al. Drews [7] tries to generalize this approach by using a Convolutional LSTM (Conv-LSTM) [32] and a softmax attention mechanism and shows this method working on previously unseen tracks. Try qxcv/rllab on the minor-fixes branch (which adds some missing hooks & addresses some bugs in the original RLLab). One way is to assume the robot to be larger than its actual size; this is equivalent to putting safety margins around the robot. We then tuned MPPI with this model and drove it around Track B successfully for 10 laps straight before being manually stopped. To solve this problem, we can incorporate a recurrent framework so that we can predict further into the future and find a better global solution. Given. [23] introduced an online Data Aggregation (DAgger) method, which mixes the expert’s policy and the learner’s policy to explore various situations like ϵ-greedy. While this formulation can be easy to write, IRL can be considered a harder problem to solve than RL. They show this system can perform similarly or better than a system trained on real-world data alone from real drones. Inverse Reinforcement Learning allows us to demonstrate desired behaviour to an agent and attempts to enable the agent to infer our goal from the demonstrations. Increasing the size of the blur will generate a more risk-averse costmap for an optimal controller. This allows MPPI to compute trajectories that are better globally. and the matrix Tw→r, transforming the world coordinates to the robot coordinates by translation and rotation, is calculated as. 1). Markov Decision Processes are used as a framework for modeling both Reinforcement Learning (RL) and Inverse Reinforcement Learning (IRL) problems [20]. Finally, we subtract [v′,u′] from [w2,h] and get the final [u,v]: We still use the same system dynamics in Eq. [22] constructed a CNN that takes in RGB images and spits out control actions of throttle and steering angles for an autonomous vehicle. K=2,vdx=5.0m/s for off-road driving, Through the coordinate transform at every timestep, the MPPI-planned final future state trajectory mapped in image space on our costmap looks like Fig. The track cost depends on the costmap and it is a binary grid map (0, 1) describes occupancy of features we want to avoid driving through, e.g. Read this paper on arXiv.org. F is assumed to be time-invariant and a finite time-horizon t∈[0,1,2,...T−1] has the unit of time determined by the control frequency of the system. This enables us to run the controller directly on the output costmap of the network. The goal of RL is to learn a policy π:X→U that achieves the maximum expected reward ∑t=1γtrt. [14, 13, 15] demonstrated failure cases of deep end-to-end controllers; the controllers failed to predict a correct label from a novel (out-of-training-data) input and there was no way to tell the output prediction is trustworthy without considering the Bayesian technique. Let us define roll, pitch, yaw angles as ϕ,θ,ψ and the camera (vehicle) position Ucam,Vcam,Wcam in the world coordinates. The full system is then able to drive around the real world version of the complex track in an aggressive fashion without crashing. Supplementary video: https://youtu.be/WyJfT5lc0aQ. manipulator, drone), are possible applications of the proposed approach. The training data collected from an optimal expert does not usually include demonstrations of failure cases in unsafe situations. The parameters we used for AIRL’s MPPI in image space for all trials are as follows: Recall that: 1. Increasing the size of the blur will generate a more risk-averse costmap for an optimal controller. This architecture provides better observability into the learning process as compared to traditional end-to-end (E2E) control approaches [22]. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, A survey of inverse reinforcement learning: challenges, methods and progress, M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang, X. Zhang, J. Zhao, and K. Zieba, End to End Learning for Self-Driving Cars, C. Chen, A. Seff, A. Kornhauser, and J. Xiao, DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving, Proceedings of 15th IEEE International Conference on Computer Vision, P. Drews, G. Williams, B. Goldfain, E. A. Theodorou, and J. M. Rehg, 1st Annual Conference on Robot Learning, CoRL 2017, Mountain View, Its extension to multi-agent settings, however, is difficult due to the more complex notions of rational behaviors. Boots, Agile Autonomous Driving using End-to-End Deep Imitation Learning, A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning, Proceedings of the 14th International Conference on Artificial Intelligence and Statistics, Learning agents for uncertain environments, Proceedings of the eleventh annual conference on Computational learning theory, W. Samek, G. Montavon, A. Vedaldi, L. K. Hansen, and K. Müller, Explainable ai: interpreting, explaining and visualizing deep learning. Surprisingly, E2EIL was able to drive up to half of a lap on Track B. It can be considered similar to IL in that sense, as we could train agents to perform according to an expert behavior. But the reward function approximator that enables transfer … In the next section, we show the experimental results of the vanilla AIRL and leave some room for the risk-sensitive version for future works. While Inverse Reinforcement Learning (IRL) is a solution to recover reward functions from demonstrations only, these learned rewards are generally heavily \textit{entangled} with the dynamics of the environment and therefore not portable or \emph{robust} to changing environments. 5. In IRL, there is an unknown expert policy, πe, from which we receive observations in the form of (xt,ut) at time t, acting according to some optimal reward R∗.

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